workshop 7 berekening 4

*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Thu, 19 Nov 2009 11:08:31 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654179glr90l64ve27vps.htm/, Retrieved Thu, 19 Nov 2009 19:09:52 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654179glr90l64ve27vps.htm/}, year = {2009}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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5246.24 0 5170.09 4920.10 4926.65 4716.99 5283.61 0 5246.24 5170.09 4920.10 4926.65 4979.05 0 5283.61 5246.24 5170.09 4920.10 4825.20 0 4979.05 5283.61 5246.24 5170.09 4695.12 0 4825.20 4979.05 5283.61 5246.24 4711.54 0 4695.12 4825.20 4979.05 5283.61 4727.22 0 4711.54 4695.12 4825.20 4979.05 4384.96 0 4727.22 4711.54 4695.12 4825.20 4378.75 0 4384.96 4727.22 4711.54 4695.12 4472.93 0 4378.75 4384.96 4727.22 4711.54 4564.07 0 4472.93 4378.75 4384.96 4727.22 4310.54 0 4564.07 4472.93 4378.75 4384.96 4171.38 0 4310.54 4564.07 4472.93 4378.75 4049.38 0 4171.38 4310.54 4564.07 4472.93 3591.37 0 4049.38 4171.38 4310.54 4564.07 3720.46 0 3591.37 4049.38 4171.38 4310.54 4107.23 0 3720.46 3591.37 4049.38 4171.38 4101.71 0 4107.23 3720.46 3591.37 4049.38 4162.34 0 4101.71 4107.23 3720.46 3591.37 4136.22 0 4162.34 4101.71 4107.23 3720.46 4125.88 0 4136.22 4162.34 4101.71 4107.23 4031.48 0 4125.88 4136.22 4162.34 4101.71 3761.36 0 4031.48 4125.88 4136.22 4162.34 3408.56 0 3761.36 4031.48 4125.88 4 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = -98.9823530336727 -368.123436426008X[t] + 1.01130602561975Y1[t] + 0.163239051318856Y2[t] -0.210238226465953Y3[t] + 0.0225486848879357Y4[t] + 99.0580758997808M1[t] + 32.162636746199M2[t] -87.15409126129M3[t] + 89.5300852830297M4[t] + 212.316131005456M5[t] + 70.6363792115456M6[t] + 87.0167479026612M7[t] + 28.9732274452039M8[t] + 175.445037331277M9[t] + 104.148942223657M10[t] -24.9058248936815M11[t] + 3.29961068075524t + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) -98.9823530336727 128.460069 -0.7705 0.443097 0.221549 X -368.123436426008 114.42225 -3.2172 0.001825 0.000913 Y1 1.01130602561975 0.106837 9.4659 0 0 Y2 0.163239051318856 0.150892 1.0818 0.282355 0.141178 Y3 -0.210238226465953 0.150955 -1.3927 0.167294 0.083647 Y4 0.0225486848879357 0.110149 0.2047 0.838282 0.419141 M1 99.0580758997808 127.617861 0.7762 0.439755 0.219878 M2 32.162636746199 129.021558 0.2493 0.803738 0.401869 M3 -87.15409126129 128.571304 -0.6779 0.499677 0.249838 M4 89.5300852830297 128.842694 0.6949 0.489004 0.244502 M5 212.316131005456 130.911752 1.6218 0.108501 0.054251 M6 70.6363792115456 132.800481 0.5319 0.596168 0.298084 M7 87.0167479026612 128.226643 0.6786 0.499203 0.249601 M8 28.9732274452039 127.545217 0.2272 0.820838 0.410419 M9 175.445037331277 132.187026 1.3272 0.187939 0.09397 M10 104.148942223657 133.407951 0.7807 0.437134 0.218567 M11 -24.9058248936815 133.701838 -0.1863 0.852665 0.426332 t 3.29961068075524 1.526913 2.161 0.033478 0.016739 Multiple Linear Regression - Regression Statistics Multiple R 0.991889576646781 R-squared 0.98384493226053 Adjusted R-squared 0.980651488637612 F-TEST (value) 308.082762194323 F-TEST (DF numerator) 17 F-TEST (DF denominator) 86 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 261.115658716006 Sum Squared Residuals 5863599.30149564 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 5246.24 5105.66272264824 140.577277351759 2 5283.61 5165.99059612251 117.619403877489 3 4979.05 5047.49149061088 -68.4414906108815 4 4825.2 4915.19946281074 -89.999462810743 5 4695.12 4829.84008153384 -134.720081533839 6 4711.54 4599.66772316939 111.872276830607 7 4727.22 4640.1969353587 87.023064641299 8 4384.96 4627.86936261506 -242.909362615056 9 4378.75 4427.68552656915 -48.9355265691532 10 4472.93 4294.61234803367 178.317651966329 11 4564.07 4335.39797735055 228.672022649453 12 4310.54 4464.73576444979 -154.195764449786 13 4171.38 4305.63437799043 -134.254377990430 14 4049.38 4042.88172949413 6.49827050587184 15 3591.37 3836.12571535685 -244.755715356853 16 3720.46 3556.54604904229 163.913950957711 17 4107.23 3760.92727103802 346.302728961984 18 4101.71 4128.30276113591 -26.592761135906 19 4162.34 4168.06912330494 -5.72912330493626 20 4136.22 4095.33658918023 40.8834108197734 21 4125.88 4238.47154990353 -112.591549903529 22 4031.48 4142.88314474009 -111.403144740094 23 3761.36 3926.83135693442 -165.471356934416 24 3408.56 3668.03793403633 -259.477934036331 25 3228.47 3389.12605741261 -160.656057412614 26 3090.45 3140.4743433602 -50.024343360197 27 2741.14 2923.56024316067 -182.420243160672 28 2980.44 2757.66109488927 222.778905110727 29 3104.33 3093.68753756243 10.6424624375658 30 3181.57 3189.9873503425 -8.41735034250093 31 2863.86 3249.81780448963 -385.957804489627 32 2898.01 2865.72992805396 32.2800719460411 33 3112.33 2984.72952635972 127.600473640275 34 3254.33 3207.58721029746 46.7427897025351 35 3513.47 3246.07932486798 267.390675132015 36 3587.61 3515.24633010154 72.3636698984587 37 3727.45 3709.86281916731 17.5871808326886 38 3793.34 3748.51134782963 44.8286521703705 39 3817.58 3712.21273755908 105.367262440915 40 3845.13 3899.73844984517 -54.608449845174 41 3931.86 4046.94311321104 -115.083113211036 42 4197.52 3997.16033780145 200.359662198552 43 4307.13 4294.41611584289 12.7138841571073 44 4229.43 4376.27480079501 -146.844800795008 45 4362.28 4411.46413578363 -49.1841357836284 46 4217.34 4448.08205419727 -230.742054197267 47 4361.28 4216.24158192204 145.038418077961 48 4327.74 4336.67235752423 -8.9323575242332 49 4417.65 4462.07499038366 -44.4249903836563 50 4557.68 4450.4007521879 107.279247812100 51 4650.35 4500.97068855122 149.379311448784 52 4967.18 4777.87176769396 189.308232306042 53 5123.42 5212.08456848621 -88.66456848621 54 5290.85 5267.1046253435 23.7453746565051 55 5535.66 5417.0918513003 118.568148699704 56 5514.06 5611.55336334789 -97.4933633478846 57 5493.88 5747.7659461844 -253.885946184393 58 5694.83 5608.14224874169 86.6877512583095 59 5850.41 5692.37616333686 158.033836663138 60 6116.64 5914.47903355625 202.160966443753 61 6175 6268.77105087234 -93.7710508723453 62 6513.58 6279.47646964196 234.103530358036 63 6383.78 6462.93037886737 -79.150378867368 64 6673.66 6560.6497554437 113.010244556301 65 6936.61 6888.83785622556 47.7721437744357 66 7300.68 7098.62382627007 202.056173729930 67 7392.93 7465.54302254721 -72.6130225472122 68 7497.31 7514.77680617368 -17.4668061736802 69 7584.71 7714.55489776069 -129.844897760686 70 7160.79 7740.80027546532 -580.010275465315 71 7196.19 7180.73481183567 15.4551881643284 72 7245.63 7159.51899281744 86.1110071825638 73 7347.51 7408.74925574395 -61.2392557439527 74 7425.75 7439.25455214388 -13.5045521438826 75 7778.51 7409.39685833856 369.113141661441 76 7822.33 7938.59651900495 -116.266519004953 77 8181.22 8152.43003437172 28.7899656282821 78 8371.47 8311.75122035953 59.7187796404691 79 8347.71 8581.15866923071 -233.448669230713 80 8672.11 8458.97804407412 213.131955925881 81 8802.79 8901.03325442694 -98.243254426944 82 9138.46 9027.43413723667 111.025862763333 83 9123.29 9193.73911622772 -70.4491162277178 84 9023.21 8873.11491726677 150.095082733225 85 8850.41 8804.1607570581 46.2492429418934 86 8864.58 8560.23251405402 304.347485945982 87 9163.74 8451.03647319738 712.703526802619 88 8516.66 8969.94816155378 -453.288161553781 89 8553.44 8483.59702107383 69.8429789261756 90 7555.2 8214.20863729087 -659.008637290867 91 7851.22 7373.15303810825 478.066961891751 92 7442 7432.50082246027 9.49917753973168 93 7992.53 7427.44516301194 565.084836988058 94 8264.04 7764.65858128783 499.38141871217 95 7517.39 8096.05966752476 -578.669667524761 96 7200.4 7288.52467024765 -88.1246702476514 97 7193.69 6903.75796872334 289.932031276658 98 6193.58 6944.72769516577 -751.14769516577 99 5104.21 5866.00541435798 -761.795414357984 100 4800.46 4775.30873971613 25.1512602838692 101 4461.61 4626.49251649736 -164.882516497358 102 4398.59 4302.32351828679 96.2664817132106 103 4243.63 4242.25343981737 1.37656018262728 104 4293.82 4084.9002832998 208.919716700202 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.240184539492171 0.480369078984342 0.759815460507829 22 0.118660392582154 0.237320785164308 0.881339607417846 23 0.0791328230079827 0.158265646015965 0.920867176992017 24 0.0446035432221474 0.0892070864442949 0.955396456777853 25 0.0215575496888775 0.043115099377755 0.978442450311122 26 0.0095821229186285 0.019164245837257 0.990417877081371 27 0.00411183430929289 0.00822366861858577 0.995888165690707 28 0.00163029670824957 0.00326059341649915 0.99836970329175 29 0.00149239666616301 0.00298479333232602 0.998507603333837 30 0.00059386821870568 0.00118773643741136 0.999406131781294 31 0.00720247437531529 0.0144049487506306 0.992797525624685 32 0.00502935771738632 0.0100587154347726 0.994970642282614 33 0.00273817030606261 0.00547634061212522 0.997261829693937 34 0.00153548930825939 0.00307097861651879 0.99846451069174 35 0.00193598831738365 0.0038719766347673 0.998064011682616 36 0.00276908946732009 0.00553817893464019 0.99723091053268 37 0.00260144173382442 0.00520288346764883 0.997398558266176 38 0.00144932399549945 0.00289864799099889 0.9985506760045 39 0.00197019777650634 0.00394039555301267 0.998029802223494 40 0.00137458226051199 0.00274916452102398 0.998625417739488 41 0.000713365548970888 0.00142673109794178 0.99928663445103 42 0.000626039362506551 0.00125207872501310 0.999373960637493 43 0.000352458665090636 0.000704917330181272 0.99964754133491 44 0.000194179909916493 0.000388359819832986 0.999805820090083 45 9.4531137611683e-05 0.000189062275223366 0.999905468862388 46 8.43522245156038e-05 0.000168704449031208 0.999915647775484 47 5.88068766096973e-05 0.000117613753219395 0.99994119312339 48 3.11412180882889e-05 6.22824361765778e-05 0.999968858781912 49 1.61440557484732e-05 3.22881114969465e-05 0.999983855944252 50 7.75850748540692e-06 1.55170149708138e-05 0.999992241492515 51 6.26725837215696e-06 1.25345167443139e-05 0.999993732741628 52 3.1189535070213e-06 6.2379070140426e-06 0.999996881046493 53 1.56955638066552e-06 3.13911276133104e-06 0.99999843044362 54 6.42529010218063e-07 1.28505802043613e-06 0.99999935747099 55 3.60598839384855e-07 7.2119767876971e-07 0.99999963940116 56 1.89305559594072e-07 3.78611119188143e-07 0.99999981069444 57 1.83482921663495e-07 3.66965843326989e-07 0.999999816517078 58 1.02703685548449e-07 2.05407371096897e-07 0.999999897296314 59 4.35301331137472e-08 8.70602662274943e-08 0.999999956469867 60 5.73681277284943e-08 1.14736255456989e-07 0.999999942631872 61 3.29437749995893e-08 6.58875499991786e-08 0.999999967056225 62 2.74254697362237e-08 5.48509394724474e-08 0.99999997257453 63 1.44591816674560e-08 2.89183633349121e-08 0.999999985540818 64 6.0472472556768e-09 1.20944945113536e-08 0.999999993952753 65 2.10631599440343e-09 4.21263198880686e-09 0.999999997893684 66 1.22825959185453e-09 2.45651918370905e-09 0.99999999877174 67 4.97186915874119e-10 9.94373831748237e-10 0.999999999502813 68 1.90063812775519e-10 3.80127625551038e-10 0.999999999809936 69 1.52579864702703e-10 3.05159729405407e-10 0.99999999984742 70 2.49229821744063e-07 4.98459643488125e-07 0.999999750770178 71 2.17724393338283e-07 4.35448786676567e-07 0.999999782275607 72 1.23993445019537e-07 2.47986890039073e-07 0.999999876006555 73 2.66488933722338e-07 5.32977867444676e-07 0.999999733511066 74 2.90483673902845e-07 5.80967347805689e-07 0.999999709516326 75 6.7700106639264e-07 1.35400213278528e-06 0.999999322998934 76 6.20623046116621e-07 1.24124609223324e-06 0.999999379376954 77 2.84614531294319e-07 5.69229062588639e-07 0.999999715385469 78 1.25139566825248e-07 2.50279133650495e-07 0.999999874860433 79 6.77540030295558e-08 1.35508006059112e-07 0.999999932245997 80 4.25570312147019e-08 8.51140624294038e-08 0.999999957442969 81 3.26976979221641e-08 6.53953958443282e-08 0.999999967302302 82 5.09504805328779e-08 1.01900961065756e-07 0.99999994904952 83 1.43850597059107e-08 2.87701194118214e-08 0.99999998561494 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 55 0.873015873015873 NOK 5% type I error level 59 0.936507936507937 NOK 10% type I error level 60 0.952380952380952 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654179glr90l64ve27vps/10vcul1258654105.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654179glr90l64ve27vps/10vcul1258654105.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654179glr90l64ve27vps/1a4311258654105.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654179glr90l64ve27vps/1a4311258654105.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654179glr90l64ve27vps/2341h1258654105.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654179glr90l64ve27vps/2341h1258654105.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654179glr90l64ve27vps/337751258654105.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654179glr90l64ve27vps/337751258654105.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654179glr90l64ve27vps/4zcir1258654105.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654179glr90l64ve27vps/4zcir1258654105.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654179glr90l64ve27vps/5e69q1258654105.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654179glr90l64ve27vps/5e69q1258654105.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654179glr90l64ve27vps/6lzpe1258654105.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654179glr90l64ve27vps/6lzpe1258654105.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654179glr90l64ve27vps/7wzym1258654105.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654179glr90l64ve27vps/7wzym1258654105.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654179glr90l64ve27vps/8kmnd1258654105.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654179glr90l64ve27vps/8kmnd1258654105.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654179glr90l64ve27vps/9a36b1258654105.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654179glr90l64ve27vps/9a36b1258654105.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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